• The Journal of Korea Robotics Society
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• v.2 no.1
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• pp.40-47
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• 2007

Recently, with the development of service robots and with the new concept of ubiquitous world, the position estimation of mobile objects has been raised to an important problem. As pre-liminary research results, some of the localization schemes are introduced, which provide the absolute location of the moving objects subjected to large errors. To implement a precise and convenient localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter: 1. The RFID receiver gets the synchronization signal from the mobile robot and 2. The ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. Since it is not easy to install the beacons at a specific position precisely, there exists a large localization error and the installation time takes long. To overcome these problems, and provide a precise and convenient localization system, a new auto calibration algorithm is developed in this paper. Also the extended Kalman filter has been adopted for improving the localization accuracy during the mobile robot navigation. The localization accuracy improvement through the proposed auto calibration algorithm and the extended Kalman filter has been demonstrated by the real experiments.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.370-388
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• 2015

The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1162-1167
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• 2003

This paper presents a simple sensor network consists of a group of sensors, RF components, and microprocessors, to perform a distributed sensing and information transmission using wireless links. In the proposed sensor network, though each sensor node has a limited capability and a simple signal-processing engine, a group of sensor nodes can perform a various tasks through coordinated information sharing and wireless communication in a large working area. Using the capability of self-localization and tracking, we show the sensor network can be applied to localization and navigation of mobile robot in which the robot has to be coordinated effectively to perform given task in real time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.59-66
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• 2013

In this paper, we propose a probabilistic framework for model-based clustering of direction of arrival (DOA) data to obtain stable sound source localization (SSL) estimates. Model-based clustering has been shown capable of handling highly overlapped and noisy datasets, such as those involved in DOA detection. Although the Gaussian mixture model is commonly used for model-based clustering, we propose use of the von Mises mixture model as more befitting circular DOA data than a Gaussian distribution. The EM framework for the von Mises mixture model in a unit hyper sphere is degenerated for the 2D case and used as such in the proposed method. We also use a histogram of the dataset to initialize the number of clusters and the initial values of parameters, thereby saving calculation time and improving the efficiency. Experiments using simulated and real-world datasets demonstrate the performance of the proposed method.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.179-188
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• 2018

In order to overcome the problems of contact palmprint recognition, a non-contact palmprint recognition system is developed on personal computer (PC) platform. Three methods, namely "double-line-single-point" (DLSP), "double-assistant-crosshair" (DAC) and "none-assistant-graphic" (NAG), are implemented for the palmprint localization to solve the severe technical challenges, including the complex background, variant illuminations, uncontrollable locations and gestures of hands. In NAG, hand segmentation and the cropping of region of interest are performed without any assistant graphics. The convex hull contour of hand helps detect the outside contour of little finger as well as the valley bottom between thumb and index finger. The three methods of palmprint localization have good operating efficiency and can meet the performance requirements of real-time system. Furthermore, an attendance system on PC platform is designed and developed based on non-contact palmprint recognition.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.3
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• pp.137-144
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• 2016

In this paper, the performance of ranging measurement, which is generated using two receiver clock offsets in one receiver, was analyzed. A spoofer transmits a counterfeited spoofing signal which is similar to the GPS signal with hostile purposes, so the same tracking technique can be applied to the spoofing signal. The multi-correlator can generate two receiver clock offsets in one receiver. The difference between these two clock offsets consists of the path length from the spoofer to the receiver and the delay of spoofer system. Thus, in this paper, the ranging measurement was evaluated by the spoofer localization performance based on the time-of-arrival (TOA) technique. The results of simulation and real-world experiments show that the position and the system clock offset of the spoofer could be estimated successfully.

• Journal of Positioning, Navigation, and Timing
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• v.6 no.1
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• pp.9-15
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• 2017

In this paper, results of a jammer detection and localization system operation are given. The system consists of receiver stations, a central tracking station, and a monitoring station and it was developed by our institute in 2014. Through real-time tests, it is confirmed that the developed system has an ability to estimate the location of interference sources with an accuracy of 50 m (CEP) even they was 10 km away. After verification, this system was installed in Incheon International Airport and operating results are being monitored by the airport and our institute continuously. In this year, there were some events that jamming signals were received from North Korea, so the data were analyzed and given here.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.205-206
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• 2023

This paper presents a new method for indoor localization track workers in construction sites. While GPS and NTRIP are effective for outdoor positioning, they are less accurate when used indoors. To address this issue, the proposed method utilizes Aruco markers to measure the distance between workers and the markers. By collecting data values, the location of each worker can be determined in real-time with high accuracy. This approach has the potential to enhance work efficiency and safety at construction sites, as it provides more precise indoor positioning compared to conventional methods, leading to improved work efficiency.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.114-122
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• 2011

We propose an automatic localization technique based on Uniform Arc Length Sampling (UALS) of 360 degree range sensor data. The proposed method samples 3D points from dense a point-cloud which is acquired by the sensor, registers the sampled points to a digital surface model(DSM) in real-time, and determines the location of an Unmanned Ground Vehicle(UGV). To reduce the sampling and registration time of a sequence of dense range data, 3D range points are sampled uniformly in terms of ground sample distance. Using the proposed method, we can reduce the number of 3D points while maintaining their uniformity over range data. We compare the registration speed and accuracy of the proposed method with a conventional sample method. Through several experiments by changing the number of sampling points, we analyze the speed and accuracy of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7A
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• pp.654-661
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• 2011

RTLS (Real-time locating systems) are used for tracking the location of people or assets in real time. In this system, RTLS readers continuously communicate with RTLS tags for measuring time or ranges and location engine tries to calculate accurate location of tags. However, when we attempt to apply this system to real world, the non-line-of-sight(NLOS) problem can be critical to the system performance because of the obstacles. In this paper, we suggest a new location estimation method for an NLOS environment using a reader-selection strategy. We have implemented all components of the locating system and carried out experiments in a test-bed. The accuracy of the system is 50% better than that of the existing general locating system.